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<\/figure>\n\n\n\nGoogle VaultGemma is a privacy-focused open language model developed by Google as part of the Gemma model family. It is designed to enable developers and organizations to build AI systems<\/a> that handle sensitive data while reducing the risk of information leakage. VaultGemma incorporates differential privacy training techniques, which introduce controlled noise during training to prevent the model from memorizing or revealing specific data points from the training dataset. <\/p>\n\n\n\n Getting started with VaultGemma is relatively straightforward because the model is openly accessible through Hugging Face<\/a>-compatible tooling and Kaggle\u2019s model hub. VaultGemma is designed as a privacy-preserving language model, which means it is trained using techniques such as differential privacy to reduce the risk of memorizing or leaking sensitive training data.<\/p>\n\n\n\n Developers can run VaultGemma locally or in cloud environments using common machine learning frameworks such as PyTorch<\/a> and Hugging Face Transformers.<\/p>\n\n\n\n Before running the model, you need to install the required Python libraries<\/a>. These libraries provide the infrastructure needed for downloading, loading, and running large language models.<\/p>\n\n\n\n The key dependencies include:<\/p>\n\n\n\n Install the libraries using pip:<\/p>\n\n\n\n If you are working in a notebook environment such as Google Colab or a web IDE, install the preview version of Transformers that supports VaultGemma.<\/p>\n\n\n\n Depending on your setup, you may also install:<\/p>\n\n\n\n Example:<\/p>\n\n\n\n pip install accelerate bitsandbytes datasets<\/p>\n\n\n\n These tools are useful when deploying the model in production or resource-constrained environments.<\/p>\n\n\n\n After installing the dependencies, the next step is to download and load the VaultGemma model.<\/p>\n\n\n\n VaultGemma models are distributed in multiple parameter sizes, such as 1B parameters, which allows developers to run them on consumer GPUs or even high-RAM CPUs.<\/p>\n\n\n\n Using Kagglehub, you can directly download the model files and initialize them using Hugging Face Transformers.<\/p>\n\n\n\n Example:<\/p>\n\n\n\n Model Download: <\/strong>Kagglehub.model_download() retrieves the VaultGemma model files from Kaggle\u2019s model repository and stores them locally.<\/p>\n\n\n\n These files typically include:<\/p>\n\n\n\n Tokenizer Initialization: <\/strong>The tokenizer converts human-readable text into tokens, which are numerical representations that the neural network can process.<\/p>\n\n\n\n Model Loading: <\/strong>The AutoModelForCausalLM class loads the model architecture designed for causal language modeling, which is used for text generation tasks.<\/p>\n\n\n\n Key parameters:<\/p>\n\n\n\n Automatically selects the optimal numeric precision such as float16 or bfloat16 for faster inference and reduced memory usage.<\/p>\n\n\n\n Learn how large language models work and how developers build real-world AI applications with them. Enroll in HCL GUVI\u2019s <\/em>LLMs and Their Applications course<\/em><\/a> to understand LLM architectures, prompt engineering, and practical AI workflows through structured online modules designed for modern AI development.<\/em><\/p>\n\n\n\n Once the model is loaded, you can begin generating text using prompts.<\/p>\n\n\n\n Large language models<\/a> generate responses through autoregressive decoding, where the model predicts the next token repeatedly until a stopping condition is reached.<\/p>\n\n\n\n Example:<\/p>\n\n\n\n This produces PyTorch tensors that the model can process.<\/p>\n\n\n\n The generate() method performs inference and predicts the next sequence of tokens.<\/p>\n\n\n\nGetting Started with VaultGemma<\/strong><\/h2>\n\n\n\n
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<\/figure>\n\n\n\nStep 1: Install the Required Libraries<\/strong><\/h3>\n\n\n\n
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pip install transformers\n\npip install Kagglehub<\/code><\/pre>\n\n\n\n!pip install git+https:\/\/github.com\/huggingface\/transformers@v4.56.1-Vault-Gemma-preview -q<\/code><\/pre>\n\n\n\nOptional Dependencies<\/strong><\/h4>\n\n\n\n
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Step 2: Download and Load the Model<\/strong><\/h3>\n\n\n\n
import Kagglehub\n\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\n# Download the VaultGemma 1B model\n\nMODEL_PATH = Kagglehub.model_download(\"google\/vaultgemma\/transformers\/1b\")\n\n# Load tokenizer\n\ntokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)\n\n# Load model\n\nmodel = AutoModelForCausalLM.from_pretrained(\n\n MODEL_PATH,\n\n device_map=\"auto\",\n\n torch_dtype=\"auto\"\n\n)<\/code><\/pre>\n\n\n\nWhat happens in this step?<\/strong><\/h3>\n\n\n\n
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device_map=\"auto\"<\/code><\/pre>\n\n\n\nStep 3: Run Prompts and Generate Responses<\/strong><\/h3>\n\n\n\n
text = \"Explain differential privacy in simple terms.\"\n\ninput_ids = tokenizer(text, return_tensors=\"pt\").to(model.device)\n\noutputs = model.generate(\n\n **input_ids,\n\n max_new_tokens=100\n\n)\n\nprint(tokenizer.decode(outputs[0], skip_special_tokens=True))<\/code><\/pre>\n\n\n\nWhat this code does<\/strong><\/h3>\n\n\n\n
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tokenizer(text, return_tensors=\"pt\")<\/code><\/pre>\n\n\n\n\n
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